Three ways to work with us.
Whichever one you pick, the work runs in your cloud accounts, on your model keys, and goes through your security review. You own the code, models, prompts, and documentation, and your team keeps running it after we've gone.
Where are you today?
Start with
AI Enablement Sprint
12 weeks
AI Enablement Sprint
For teams with a business problem, an executive sponsor, and no production AI yet.
Weeks 1–2
Sprint Zero
We map your processes, data flows, and PII boundaries, then run every candidate use case through the AI Necessity Gate. Some of them need a model. Others are better served by a well-built deterministic pipeline, which is faster, cheaper, and easier to audit. We put our recommendation in writing, with ROI measured in value created rather than hours saved.
Weeks 3–7
Build
We're working on your real data from week three. We compare models across providers and demo progress every Friday.
Weeks 8–10
Harden
We add error handling, observability, audit logging, and cost ceilings, and write a security validation memo for your InfoSec team.
Weeks 11–12
Enable
Runbooks, training, and on-call shadowing, so your team is already operating the system before we leave.
What you provide
A business problem and a sponsor who can give 2–4 hours a week, plus a data expert and time for stakeholder interviews.
What you own after
The working system, the code, the architecture, and a team that knows how to run it.
Example: in one quarter with an animal-health platform, we put the data foundation in order and built a clinical documentation tool on top of it. The tool itself took ten weeks, and it now writes more than 900 summaries a month on a foundation the security team had already approved.
Read the case studyRapid AI Development
For teams with a defined use case and a technical spec.
There's no Sprint Zero here. Senior engineers start on architecture and code in the first week.
What we build
Built in your AWS, Azure, or GCP accounts with your Anthropic, OpenAI, or Gemini keys.
What ships
- Architecture and solution design
- Build and integration
- Production hardening with observability and cost controls
- Documentation and handoff
Recent builds
8,000 docs/day
Invoice and PO processing without templates
99.5% accuracy
40% faster
Open-text product search
quoting
7 models
Store-level forecasting
tournament at store-SKU granularity
Multi-agent
QBR facilitation system
on Vertex AI
Fractional AI Team
For companies that need senior AI and data platform people for three to twelve months rather than a single project.
Embedded Team
One or more senior engineers inside your engineering or data organization.
Typical mandates
- Standing up a governed event spine and Schema Registry
- Tokenization of sensitive data with Cloud DLP or equivalent
- AI-enhanced SDLC with spec-driven development and secure-coding guardrails
- Model evaluation and cost governance
- Governed agents with evaluation and cost controls
- The first production use cases on that foundation
Platform Advisory
Architecture review and decision support for a CTO or head of data running their own program.
Typical decisions
- Event-driven vs. CDC
- Warehouse ingestion paths
- Data classification and tokenization strategy
- Agent governance
How it works: our engineers work in your repos and join your standups, and we judge ourselves by how quickly your team can run the platform without us.
Talk to an ArchitectAgents · part of Rapid AI Development
Agents that pass security review.
Most "agent" projects don't need an agent. What we're usually shown is a fixed workflow with a chat box on top, and that's fine: build it as a workflow. It's cheaper to run, easier to test, and gives your security team nothing to lose sleep over. We save agents for problems that need planning, tool use, and reasoning over retrieved context, and we tell you which one you have before we write any code.
Plenty of firms will build you an agent. Getting it approved by InfoSec, compliance, and the CFO is the hard part, and Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027 over cost, unclear value, or risk (Gartner, June 2025). We design for that review from the first week.
Our reference pattern
Generate
A generator agent drafts.
Critique
A critique agent verifies every claim against retrieved source passages.
Refine
A refine agent produces the final output with citations.
Guardrails around every run
- PII masked by Cloud DLP before storage
- Dry runs scored against a grounding rubric
- VPC Service Controls perimeter
- CMEK + least-privilege service accounts
- Distributed tracing on every run
- Unsupported-claim-rate alert
What this means in practice
The agent only ever sees tokens, never real identities. Each claim in the output is checked against a source, every run is traced, and your security team gets a written memo before anything goes live.
Compare side by side
Which engagement fits where you are
Final scope confirmed in the SOW after the Architecture Review.
AI Enablement Sprint
- Best when
- You have a business problem, an executive sponsor, and no production AI yet
- You provide
- A business problem, a sponsor for 2–4 hours a week, a data expert, and time for stakeholder interviews
- We deliver
- Sprint Zero + build + production hardening + enablement
- First deliverable
- Sprint Zero build plan through the AI Necessity Gate (Wk 2)
Rapid AI Development
- Best when
- You have a defined use case and a technical spec
- You provide
- A defined use case, a technical spec, and a technical lead
- We deliver
- Architecture, build, integration, hardening, and handoff. We go straight to code
- First deliverable
- Working prototype (Wk 3–4)
Fractional AI Team
- Best when
- You need senior AI and data platform people for 3–12 months
- You provide
- A mandate, a product owner, and access to your repos and standups
- We deliver
- Senior engineers embedded in your team, or platform advisory for your CTO
- First deliverable
- First sprint output (Wk 1–2)
For your procurement team
What's in a typical SOW
Eight things every FusionLeap SOW covers. Bring this to your CFO, security lead, and counsel before the Architecture Review so they can flag concerns early.
Scope of work
The working systems we'll deliver, what's in and out of scope, and acceptance criteria.
Timeline & milestones
Phase boundaries, Friday demo cadence, hand-off date.
Commercial terms
Fixed scope for Sprint and Rapid AI Development, monthly for Fractional. Net 30 standard.
IP ownership
Client owns all custom code, models, prompts, and documentation. We retain rights only to FusionLeap internal tooling.
Compliance scope
Engineering responsibility (us) vs. legal and audit responsibility (your team), spelled out in one clause.
Change control
How we change scope mid-engagement, including adding work once we've started.
Confidentiality & security
Mutual NDA signed before the SOW. Sub-processor list disclosed. Data handling per your security team's spec.
Termination & handover
Either side can end the engagement, with or without cause, and you get everything built to date.
The full SOW is tailored to your engagement before kickoff, and we'll walk you through every clause line-by-line.
FAQ
The questions buyers actually ask
Procurement, security, and engineering questions we answer up front so they don't derail the engagement.
Those engagements usually open with a strategy phase that ends in a roadmap, delivered by a large team. Ours open with real work on your data from week three, built by the same senior people who scoped it. If you need a program at that scale, we're not the right fit and we'll tell you so.
Not sure which engagement fits?
That's what the Architecture Review is for. Tell us what you're trying to do and we'll suggest an engagement, or tell you plainly if we're not the right fit.
Talk to an Architect